Human3.6M and 3DPW.
In Table REF , we compare the performance of our method and prior arts on the Human3.6M [1]} and 3DPW [2]} datasets.
For VisDB and I2L-MeshNet [3]}, we report the results of both heatmap-based mesh outputs (mesh) and SMPL parameters (param).
Our SMPL parameters are obtained from regression and test-time optimization.
Note that each method uses different network backbone, human body representation, training datasets, and inference strategy.
For instance, METRO [4]} and Mesh Graphormer [5]} adopt a transformer-based [6]} network while the others use CNN backbones.
VIBE [7]} and TCMR [8]} are video-based approaches whereas the others only take images as input.
Despite these differences, VisDB performs favorably against prior methods in term of most evaluation metrics.
Particularly, our method achieves larger performance gains on the 3DPW dataset since it contains more truncation and occlusion cases.
The VisDB performance is most directly comparable with I2L-MeshNet [3]} as we adopt similar training settings.
For fair comparisons, we re-train its model on the same datasets and denote it as I2L-MeshNet\(^\dagger \) .
The results demonstrate that our visibility learning improves both the mesh and SMPL outputs significantly.
In prior literature, SMPL parameters generally lead to higher errors compared to dense mesh outputs, which we conjecture is caused by the difficulty to directly regress low-dimensional parameters.
On the contrary, VisDB is a powerful intermediate representation that provides dense 3D information of visible partial body, allowing us to regress and optimize SMPL parameters more accurately.
In our experiments, we observe that VisDB (mesh) captures the human silhouettes better but VisDB (param) produces lower errors since the ground-truth meshes are also regularized by SMPL representation.
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